π€Reddit r/MachineLearningβ’Stalecollected in 5h
zer0dex Dual-Memory Beats RAG 91.2% Recall
#local-agents#memory-systems#vector-retrievalzer0dexzer0dexchromadbollamarag
π‘91.2% offline LLM recall > RAGβdual memory for local agents (GitHub)
β‘ 30-Second TL;DR
What Changed
Layer 1: ~800-token compressed markdown semantic index always in context
Why It Matters
Advances local agent memory, closing gap to cloud RAG without infrastructure needs. Semantic index enables structured recall, ideal for edge deployments.
What To Do Next
pip install zer0dex and test recall on your local Ollama agent benchmarks.
Who should care:Developers & AI Engineers
Key Points
- β’Layer 1: ~800-token compressed markdown semantic index always in context
- β’Layer 2: ChromaDB with 70ms pre-message HTTP hook for top-k injection
- β’91.2% recall on local Ollama vs 80.3% full RAG, preserves relational structure
- β’Fully offline, no cloud; 11pp gap from topology preservation
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Original source: Reddit r/MachineLearning β
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